FABIA: factor analysis for bicluster acquisition

Funding: Janssen Pharmaceutica N.V. and Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT project 80536).

[1]  George M. Church,et al.  Biclustering of Expression Data , 2000, ISMB.

[2]  Aapo Hyvärinen,et al.  A Fast Fixed-Point Algorithm for Independent Component Analysis , 1997, Neural Computation.

[3]  Arlindo L. Oliveira,et al.  A polynomial time biclustering algorithm for finding approximate expression patterns in gene expression time series , 2009, Algorithms for Molecular Biology.

[4]  Ron Shamir,et al.  EXPANDER – an integrative program suite for microarray data analysis , 2005, BMC Bioinformatics.

[5]  L. Lazzeroni Plaid models for gene expression data , 2000 .

[6]  George K. Karagiannidis,et al.  Distributions Involving Correlated Generalized Gamma Variables , 2007 .

[7]  Andrea Califano,et al.  Analysis of Gene Expression Microarrays for Phenotype Classification , 2000, ISMB.

[8]  Sven Bergmann,et al.  Defining transcription modules using large-scale gene expression data , 2004, Bioinform..

[9]  Hong Yan,et al.  Discovering biclusters in gene expression data based on high-dimensional linear geometries , 2008, BMC Bioinformatics.

[10]  Patrik O. Hoyer,et al.  Non-negative Matrix Factorization with Sparseness Constraints , 2004, J. Mach. Learn. Res..

[11]  A. Orth,et al.  Large-scale analysis of the human and mouse transcriptomes , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[12]  Ying Xu,et al.  QUBIC: a qualitative biclustering algorithm for analyses of gene expression data , 2009, Nucleic acids research.

[13]  Arlindo L. Oliveira,et al.  Biclustering algorithms for biological data analysis: a survey , 2004, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[14]  David J. Reiss,et al.  Integrated biclustering of heterogeneous genome-wide datasets for the inference of global regulatory networks , 2006, BMC Bioinformatics.

[15]  Mark A. Girolami,et al.  A Variational Method for Learning Sparse and Overcomplete Representations , 2001, Neural Computation.

[16]  Jun S Liu,et al.  Bayesian biclustering of gene expression data , 2008, BMC Genomics.

[17]  Yudong D. He,et al.  Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.

[18]  Eckart Zitzler,et al.  BicAT: a biclustering analysis toolbox , 2006, Bioinform..

[19]  Johanna Hardin,et al.  A note on oligonucleotide expression values not being normally distributed. , 2009, Biostatistics.

[20]  S. Kaski,et al.  Bayesian biclustering with the plaid model , 2008, 2008 IEEE Workshop on Machine Learning for Signal Processing.

[21]  Richard M. Karp,et al.  Discovering local structure in gene expression data: the order-preserving submatrix problem. , 2003 .

[22]  Brian Everitt,et al.  An Introduction to Latent Variable Models , 1984 .

[23]  Philip S. Yu,et al.  An Improved Biclustering Method for Analyzing Gene Expression Profiles , 2005, Int. J. Artif. Intell. Tools.

[24]  Roded Sharan,et al.  Discovering statistically significant biclusters in gene expression data , 2002, ISMB.

[25]  Wojtek J. Krzanowski,et al.  Improved biclustering of microarray data demonstrated through systematic performance tests , 2005, Comput. Stat. Data Anal..

[26]  Arlindo L. Oliveira,et al.  Identification of Regulatory Modules in Time Series Gene Expression Data Using a Linear Time Biclustering Algorithm , 2010, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[27]  Friedrich Leisch,et al.  A toolbox for bicluster analysis in R , 2008 .

[28]  L. Staudt,et al.  The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. , 2002, The New England journal of medicine.

[29]  Philip S. Yu,et al.  Clustering by pattern similarity in large data sets , 2002, SIGMOD '02.

[30]  Klaus Obermayer,et al.  A new summarization method for affymetrix probe level data , 2006, Bioinform..

[31]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[32]  Lothar Thiele,et al.  A systematic comparison and evaluation of biclustering methods for gene expression data , 2006, Bioinform..

[33]  Ben Taskar,et al.  Learning Probabilistic Models of Link Structure , 2003, J. Mach. Learn. Res..

[34]  Hinrich W. H. Göhlmann,et al.  I/NI-calls for the exclusion of non-informative genes: a highly effective filtering tool for microarray data , 2007, Bioinform..

[35]  G. Getz,et al.  Coupled two-way clustering analysis of gene microarray data. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[36]  Jill P. Mesirov,et al.  Subclass Mapping: Identifying Common Subtypes in Independent Disease Data Sets , 2007, PloS one.

[37]  Bhaskar D. Rao,et al.  Variational EM Algorithms for Non-Gaussian Latent Variable Models , 2005, NIPS.

[38]  J. Hartigan Direct Clustering of a Data Matrix , 1972 .

[39]  Aidong Zhang,et al.  Interrelated two-way clustering: an unsupervised approach for gene expression data analysis , 2001, Proceedings 2nd Annual IEEE International Symposium on Bioinformatics and Bioengineering (BIBE 2001).

[40]  J. Munkres ALGORITHMS FOR THE ASSIGNMENT AND TRANSIORTATION tROBLEMS* , 1957 .

[41]  T. M. Murali,et al.  Extracting Conserved Gene Expression Motifs from Gene Expression Data , 2002, Pacific Symposium on Biocomputing.

[42]  Joseph T. Chang,et al.  Spectral biclustering of microarray data: coclustering genes and conditions. , 2003, Genome research.